The OpenSat4Weather dataset: Ku-band satellite link data for precipitation monitoring

Abstract. The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for identifying and quantifying precipitation. Hence, SMLs can serve as low-cost rainfall sensors complementary to conventional instruments. However, a significant challenge for opportunistic sensors, such as SMLs and their terrestrial counterpart, i.e., commercial microwave links (CMLs), stems from potential ownership issues, possibly hindering progress in the development of processing tools and validation studies. This underscores the critical need for open data. While CML open datasets are already available, there are no large SML datasets in public repositories. To fill this gap, we introduce here the OpenSat4Weather dataset, a comprehensive and openly accessible collection of data from 215 SML sensors located in Southern France, covering a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166. OpenSat4Weather also includes concurrent conventional data: 6-minute rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data are derived from the gauge-adjusted weather radar product Panthere from Météo-France. Additionally, ERA5 reanalysis data of the 0-degree isotherm height are provided for rain height estimation, which is essential for accurate conversion of the received signal level into rainfall intensity.

In this paper, we overview the OpenSat4Weather dataset. We detail the data preparation process and draw statistics of data availability. Furthermore, we present a descriptive analysis of the dataset, including an assessment of the observed rain characteristics, based on the rain gauges, and of the SML received power, and a comparison between SML and radar data. Finally, we provide examples of disturbances and anomalous patterns encountered on the SML raw data. Our ultimate goal is to promote open research that can help in accelerating the development of SML-based applications. Indeed, enhancing rainfall monitoring capabilities by opportunistic sensors could be beneficial in those areas where conventional networks are scarce.